粒子群优化相机标定的python代码

以下是使用粒子群优化算法进行相机标定的Python代码：

import numpy as np
import cv2
from scipy.optimize import minimize
from functools import partial

class ParticleSwarmOptimization:
    def __init__(self, n_particles, dim, bounds, cost_func, w=0.5, c1=1, c2=2):
        self.n_particles = n_particles
        self.dim = dim
        self.bounds = bounds
        self.cost_func = cost_func
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.best_positions = np.zeros((n_particles, dim))
        self.best_costs = np.ones(n_particles) * np.inf
        self.swarm_positions = np.zeros((n_particles, dim))
        self.swarm_velocities = np.zeros((n_particles, dim))
        self.iteration = 0

    def optimize(self, n_iterations):
        for i in range(self.n_particles):
            self.swarm_positions[i] = np.random.uniform(self.bounds[0], self.bounds[1], self.dim)

        for i in range(n_iterations):
            self.iteration = i
            costs = self.cost_func(self.swarm_positions)
            for j in range(self.n_particles):
                if costs[j] < self.best_costs[j]:
                    self.best_costs[j] = costs[j]
                    self.best_positions[j] = self.swarm_positions[j]

            for j in range(self.n_particles):
                r1, r2 = np.random.uniform(0, 1, 2)
                cognitive = self.c1 * r1 * (self.best_positions[j] - self.swarm_positions[j])
                social = self.c2 * r2 * (self.best_positions.max(axis=0) - self.swarm_positions[j])
                self.swarm_velocities[j] = self.w * self.swarm_velocities[j] + cognitive + social
                self.swarm_positions[j] += self.swarm_velocities[j]

    def get_best_position(self):
        return self.best_positions[self.best_costs.argmin()]

def calibrate_camera(objpoints, imgpoints, img_shape):
    def cost_func(params, objpoints, imgpoints):
        mtx = np.array([params[0], 0, params[1], 0, params[2], params[3], 0, 0, 1]).reshape((3, 3))
        dist = params[4:]
        err = 0
        for i in range(len(objpoints)):
            imgpoints2, _ = cv2.projectPoints(objpoints[i], np.eye(3), np.zeros((3, 1)), mtx, dist)
            err += np.sum((imgpoints[i] - imgpoints2)**2)
        return err

    n_corners = np.prod(objpoints[0].shape[:2])
    objp = np.zeros((n_corners, 3), np.float32)
    objp[:, :2] = np.mgrid[0:8, 0:6].T.reshape(-1, 2)

    ps = ParticleSwarmOptimization(n_particles=50, dim=8+n_corners, bounds=[-1, 1], cost_func=partial(cost_func, objpoints=objpoints, imgpoints=imgpoints))
    ps.optimize(n_iterations=100)
    params = ps.get_best_position()

    mtx = np.array([params[0], 0, params[1], 0, params[2], params[3], 0, 0, 1]).reshape((3, 3))
    dist = params[4:]
    return mtx, dist

objpoints = []
imgpoints = []
img_shape = None
for i in range(1, 21):
    img = cv2.imread(f'images/img{i:02d}.jpg')
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_shape = gray.shape[::-1]

    ret, corners = cv2.findChessboardCorners(gray, (8, 6), None)
    if ret:
        objpoints.append(objp)
        imgpoints.append(corners)

mtx, dist = calibrate_camera(objpoints, imgpoints, img_shape)
print(mtx)
print(dist)

该代码首先定义了一个`ParticleSwarmOptimization`类来实现粒子群优化算法。在`optimize`方法中，先随机生成粒子位置，然后根据当前位置计算代价函数，更新个体最优位置和全局最优位置，然后更新粒子速度和位置。最后，调用`get_best_position`方法返回全局最优位置。

接下来，定义了一个`calibrate_camera`函数来进行相机标定。该函数的代价函数是将相机参数和畸变系数作为输入，然后将每个标定图像的标定点投影到图像平面，并计算投影误差的平方和。该函数使用`minimize`函数来寻找最优参数。在该函数中，首先定义了棋盘格的三维坐标，然后对于每个标定图像，使用`cv2.projectPoints`函数将三维坐标投影到图像平面，并计算投影误差。最后，使用`ParticleSwarmOptimization`类来优化相机参数和畸变系数。

最后，读取标定图像并调用`calibrate_camera`函数进行相机标定。